Generating high-fidelity visual content is a crucial task in computer vision. Over the past few years, deep generative models such as [1]}, [2]}, [3]}, [4]}, [5]} have improved the fidelity of generated content to a great extent. Generative Adversarial Networks (GANs) which were first introduced in [4]} have be been a powerful paradigm for learning high dimensional data distributions. In the field of computer vision, GANs have been employed for different tasks for content synthesis, including unconditional image synthesis [4]}, [8]}, image synthesis conditioned on text [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]}, generating text description conditioned on images [20]}, style transfer between images [21]},pose transfer in person images  [22]}, [23]}, [24]}, [25]}, and human pose synthesis conditioned on text [26]}. In [27]}, the authors generate images based on text and show that using a sparse set of keypoints makes synthesizing a higher resolution image possible. Zhou et al. [25]} alter the pose of a person in a given image based on a text description. The approach assumes that all poses can be represented by a set of clusters and is applied to a pedestrian dataset in which the poses are of standing or walking persons, and therefore cannot be applied to datasets with complex poses such as COCO.
Li et al. [9]} propose an object-driven attention module that relies on an object classifier loss. However, the method fails to generate plausible human shapes despite improved results.
In fashion applications, Zhu et al. [30]} manipulate the clothing of a person in a given image based on a text description without altering the pose.
Prokudin et al.[31]} propose generating photorealistic images of humans using a sparse set of 3D vertices and a map of their corresponding RGB colors and depth values.
Other related works such as [32]}, [33]}, [34]}, [35]} deal with searching for or synthesizing plausible human poses that match object affordances in a given scene.
Recurrent GAN architectures for generating sequential data was introduced in [36]} and applied to music data. In a subsequent work, Hyland et al.[37]} included a conditional input and applied it for generating time-series data in the medical domain.
